Elegant Solutions for Fingerprint Image Enhancement

ABSTRACT

This invention includes image processing techniques directed to achieve feature enhancement and background-foreground enhancement in fingerprint images. The image is divided into plural segments depending on the ridge-valley directions. Each segment is separately filtered with a directional filter generally perpendicular to the corresponding ridge-valley direction. Background-foreground detection employs edge detection to identify edge pixels. These edge pixels are averaged to determine a threshold. The threshold is applied to the original image to determine background and foreground pixels. The background and foreground pixels are filtered via a watershed fill filter with separate connectivity for background and foreground pixels.

TECHNICAL FIELD OF THE INVENTION

The technical field of this invention is fingerprint imaging.

BACKGROUND OF THE INVENTION

Image processing has been applied to a wide variety of image enhancementissues. There are two crucial problems in fingerprint image analysis.These are feature enhancement and background-foreground segmentation.Current feature enhancement techniques typically target feature pointsincluding minute-ridge bifurcations and ridge terminations. Thesefeatures are the objects most often scrutinized in matching algorithmsthat extract the feature points on the enrolled fingerprint and attemptto localize the feature point in the input fingerprint.

FIG. 1 illustrates the block diagram of a prior art fingerprint featurepoint based algorithm. Current techniques first generate a templatecontaining information pertinent to feature points of the fingerprints.These templates are then compared. Image input 101 receives thefingerprint input image. Noise removal stage 102 removes noise from theimage input. Feature extraction stage 103 attempts to recognize andextract relevant features form the image. Template generator 104generates templates for the matching process. Template generator 104requires a fingerprint input image free from noise so that featurepoints can be extracted with a high level of confidence.

Fingerprints captured under noisy operational environments includinginconsistent contact of finger with sensor, exertion of more thanoptimal or less than optimal required pressure on the sensor, shearforce on the sensor, and sensor defects often tend to lessen thedistinction between ridges and valleys. As a result feature extractionstage 103 tends to extract many spurious minutiae. Such spurious minutiadegrades the performance of the identification system. Conventionalapproaches to solve this degradation problem include filtering to reduceambient noise.

FIG. 2 illustrates the 2D convolution mask conventionally used for lowpass filtering the input image. Each pixel of the image is replaced withthe weighted average of the neighboring pixels. This removessmall-unwanted discontinuities present in the image. This can also blurthe image.

Low-pass median filters show excellent performance for images havingsalt and pepper noise. Unfortunately these filters blur the imagereducing the ridge-valley distinction. This effect can be observed inFIG. 3. FIG. 3A is an example input image. FIG. 3B is the correspondingfiltered output image.

Detection of ridges and valleys, an important step in the featureextraction process, involves segmentation of the image background(valleys) and foreground (ridges). In conventional techniques athresholding operation converts a gray-scale image into a black andwhite image. The threshold can be determined adaptively, but thisprocess is clearly not perfect at all times. Unwanted continuities ordiscontinuities in the ridge-valley structures often result. This occursfrom information loss during color conversion and because thresholdingis independent of pixel-neighborhood relationships. Noise added due tothis down conversion has to be reduced to achieve accurate featureextraction.

SUMMARY OF THE INVENTION

The present invention describes solutions used in image processing toachieve feature enhancement and background-foreground segmentation. Theinvention employs adaptive filtering, which has the desirablecharacteristic of allowing improvement of the image while preserving theridge valley distinctions. The invention further includes directionalfiltering which takes into consideration a direction approximate to theridge and valley orientations in the fingerprint image. This combinesthe characteristics of the low-pass filters and those of high-passfilters to highlight discontinuities.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other aspects of this invention are illustrated in thedrawings, in which:

FIG. 1 illustrates a functional block diagram of a feature point basedalgorithm (Prior Art);

FIG. 2 illustrates a conventional 2D convolution mask used for low passfiltering the input image (Prior Art);

FIGS. 3A and 3B illustrate the results observed in using low-pass medianfilters, which are effective in reducing salt and pepper noise in imagesbut cause the image to become blurred reducing the ridge-valleydistinction, on an example image (Prior Art);

FIGS. 4A, 4B and 4C illustrate a 45 degree oriented low pass filtermask, an example input image and the result of the using this filter onthis input image;

FIG. 5 illustrates an example of image quantization, where the image isseparated into eight parts with a 22.5 degree resolution;

FIGS. 6A and 6B illustrate a modified high pass filter mask includinghigh pass filters oriented perpendicular to ridge-valley lines and theresult of using this filter on the example input image;

FIG. 7A illustrates the combined filter mask used for reduction ofbackground-foreground segmentation;

FIG. 7B illustrates the result of an example image filtered using masksoriented along ridge/valley locations;

FIG. 8 illustrates a flow chart of the ridge removal process of theinvention; and

FIGS. 9A, 9B and 9C illustrate a comparison of an example input image,the thresholded image and the cleaned image after watershed filloperations.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Adaptive filtering has the desirable characteristic of allowingimprovement of the image while preserving the ridge valley distinctions.The invention includes directional filtering combining thecharacteristics of the low-pass filter and high-pass filters tohighlight the discontinuities. These filterings take place during thenoise removal stage 102 in the feature point based algorithm illustratedin FIG. 1.

FIG. 4 illustrates a modified low pass filter mask to enhance the imagein a selected direction. The example of FIG. 4 uses a 45-degree linethrough the input fingerprint image. All points on ridges and valleyswith orientations in range of 33.5 degrees through 56.0 degrees, 22.5degrees on either side of 45 degrees, are filtered with masks orientedalong the 45 degree line.

FIG. 4A illustrates a directional filter in a 5 by 5 mask array. Thecurrent pixel under consideration is at the center pixel of the array.The values in each cell are the weights given to the correspondingneighboring pixel in the weighted sum. The weighted sum of FIG. 4A is ⅕of the pixel values of the pixels along the South-West to North-Eastdiagonal, that is 45 degrees. The cell value for other pixels is zero,indicating these pixels do not contribute to the weighted sum. Thecenter pixel value is replaced with this weighted sum. Other filtermasks are possible including low but non-zero weights for off-axispixels. FIG. 4B illustrates an example input image. FIG. 4C illustratesthe result of the filtering the example image of FIG. 4B by thedirectional filter of FIG. 4A. Further improvement suppressingsmall-unwanted discontinuities present in the image resulting from useof the conventional 2D convolution mask of FIG. 2 can be obtained withthe following refinement.

This refinement uses the average pixel values in a direction parallel tothe ridge and valley orientations in the fingerprint image. This isequivalent to low pass filtering along the ridge-valley orientation.This filtering causes any blurring to be along the direction of ridgeorientations markedly preserving the ridge-valley distinction.

The input image is partitioned into a number of parts dependent onranges of ridge-valley orientation. FIG. 5 illustrates an exampleimplementation including an input image, filtered parts with orientationquantized into eight parts with a resolution of 22.5 degrees and thefinal resultant filtered image. Filtering the image with modified highpass filters oriented in a direction perpendicular to the ridge-valleyorientations enhances the ridge-valley distinctions. As an example, allpoints on ridges and valleys with orientations in range of approximately33.5 degrees through 56 degrees (in the neighborhood of 45 degrees) arefiltered with masks oriented in direction 90 degrees plus 45 degrees,which equals 135 degrees or −45 degrees. Thus each pixel is replacedwith a weighted sum of surrounding pixels. The weighted sum favorsneighboring pixels along the ridge-valley line and disfavors pixelsperpendicular to this line. FIG. 4A illustrates an example of such aweighted sum for the 45 degree angle. Similar filter masks are producedfor the other orientations. This directional filtering tends to low passfilter along the ridge-valley lines while not blurring the ridge-valleydistinction. FIG. 5 illustrates the assembled final result of thedirectional filtering of the separate segments.

FIG. 6A illustrates a modified high pass filter mask with an orientationof 45 degrees. FIG. 6B illustrates the enhanced fingerprint image asfiltered by the filter of FIG. 6A.

This invention allows selection of a threshold based on edge-informationof the image and complements it with a watershed filling operation toreduce the noise added due to improper image segmentation. Edges of thegray-scale image in the fingerprint ridge-valley boundaries containpixels whose intensity values are intermediate values between backgroundand foreground. Use of only these pixels to determine the segmentationthreshold gives better results as compared to using the entire range ofpixels. The invention first marks the edge pixels using conventionaledge detection methods. For example, using the Sobel edge detectionmethod, the invention first calculates the threshold of the enhancedimage as the average of these marked pixel intensities. The image aftersegmentation is a black and white image, with a white background and ablack foreground. This image segmentation requires the background to beonly 4-connected with the nearest horizontal and vertical neighborpixels. The foreground may have 8-connectivity with all nearest neighborpixels horizontal, vertical and diagonal included. This information isused to remove the noise due to the thresholding operation.

FIG. 7 illustrates the combined filter mask for reduction ofbackground-foreground segmentation. FIG. 7B illustrates the result ofthe example image filtered using masks oriented along ridge-valleylocations.

This invention uses the following heuristics with the watershed fillingoperation. FIG. 8 illustrates the process of removing thediscontinuities in ridges after finalizing the binary data requires.Step 801 checks for each white pixel. Step 802 uses the modifiedwatershed fill operation to connect all the four connected white pixelsto that pixel.

Step 803 checks to determine whether the number of 4-connected whitepixels to that pixel is less than a first threshold T1. If this is true(YES in step 803), then step 804 makes all the connected pixels black.If this is false (NO in step 803), then the color of these pixels isunchanged.

In either case, step 805 identifies each black pixel. Step 806 performsa watershed fill operation to connect all the 8-connected black pixelsto the black pixels identified in step 805.

Step 807 checks to determine whether the number of 8-connected blackpixels to that pixel is less than a second threshold T2. If this is true(YES in step 807), then step 808 makes all the connected pixels white.If this is false (NO in step 807), then the color of these pixels isunchanged. In either case, the ridge removal is complete in step 809.

The steps included in FIG. 8 totally eliminate the noise induced by thethresholding operation. FIG. 9 illustrates a comparison of the inputimage illustrated in FIG. 9A, the thresholded image illustrated in FIG.9B and the cleaned image after watershed fill operations illustrated inFIG. 9C.

1. A method of filtering a fingerprint image comprising the steps of:receiving an input fingerprint image; separating the input fingerprintimage into plural, distinct segments, each segment having ridge-valleylines within a predetermined range of angles; directionally filteringeach of said segments with a corresponding directional filter generallyperpendicular to said predetermined range of angles of said segment;assembling said directionally filtered segments into a reassembledimage; and outputting said reassembled image.
 2. The method of filteringa fingerprint image of claim 1, wherein: said step of directionallyfiltering each of said segments includes for each segment groupingpixels into groups parallel to the ridge-valley lines; averaging pixelvalues of pixels within each group, and replacing each pixel in eachgroup with said averaged pixel value of said group.
 3. The method offiltering a fingerprint image of claim 1, wherein: said step ofdirectionally filtering each of said segments includes for each segmentreplacing each pixel value with a weighted sum of surrounding pixelvalues, each weighted sum favoring neighboring pixels along saidridge-valley line of said segment and disfavoring neighboring pixelsperpendicular to said ridge-valley line of said segment.
 4. The methodof filtering a fingerprint image of claim 1, further comprising thesteps of: after said reassembling step, thresholding said fingerprintimage by detecting all ridge-valley edge pixels, averaging pixel valuesof said detected edge pixels, setting each pixel in said reassembledimage to foreground if said corresponding pixel value is greater thansaid average edge pixel value, and setting each pixel in saidreassembled image to background if said corresponding pixel value isless than said average edge pixel value.
 5. The method of filtering afingerprint image of claim 4, further comprising the steps of: aftersaid thresholding step, watershed filling said background pixels.
 6. Themethod of filtering a fingerprint image of claim 5, wherein: said stepof watershed filling background pixels includes for each backgroundpixel identifying other background pixels horizontally or verticallyadjacent, and replacing all said identified background pixels withforeground pixels if said identified background pixels are less innumber than a predetermined number.
 7. The method of filtering afingerprint image of claim 4, further comprising the steps of: aftersaid thresholding step, watershed filling said foreground pixels.
 8. Themethod of filtering a fingerprint image of claim 7, wherein: said stepof watershed filling foreground pixels includes for each foregroundpixel identifying other foreground pixels horizontally, vertically ordiagonally adjacent, and replacing all said identified foreground pixelswith background pixels if said identified foreground pixels are less innumber than a predetermined number.